Multi-Variable Linear Regression-Based Prediction of A Computationally-Heavy Link Stability Metric for Mobile Sensor Networks

Abstract

Until now, we were determining stable data gathering (DG) trees for mobile sensor networks (MSNs) using a link stability metric (computationally-light or computationally-heavy) that is directly computed on the egocentric edge network. Among such DG trees, the BPI' (complement of bipartivity index)-based DG trees were observed to be the most stable, but the BPI' metric is also computationally-heavy. Hence, we seek to build a multi-variable linear regression model to predict the BPI' values for the egocentric networks of edges using three computationally-light metrics (neighborhood overlap: NOVER, one-hop two-hop neighborhood: OTH, and normalized neighbor degree: NND) that are also computed on the egocentric edge networks. The training and testing are conducted as part of a single simulation run (i.e., in-situ). The training dataset comprises of the BPI', NOVER, OTH and NND values of randomly sampled egocentric edge networks during the first phase of the simulation (1/5th of the total simulation time). We observe the R-square values for the prediction to be above 0.85 for both low density and high density networks. We also observe the lifetimes of the predicted BPI'-based DG trees to be 87-92% and 55-75% of the actual BPI'-based DG trees for low-moderate and moderate-high density networks respectively.